Automatic Polyp Detection by Combining Conditional Generative Adversarial Network and Modified You-Only-Look-Once

Recent years have seen deep learning algorithms such as Convolutional Neural Networks (CNNs) and their variants achieving competitive performance in the application of medical image processing. Yet their shortcomings are: (i) the need of a large collection of annotated data, and (ii) the involvement of careful design of CNN layers. In this paper, we presented a novel polyp detection architecture based on two ideas: (i) to use Conditional Generative Adversarial Network (CGAN) to expand the training datasets, specifically the Generator takes advantage of the Efficient Spatial Pyramid (ESP) and the Discriminator is based on the PatchGAN; (ii) to modify the architecture of YOLOv4 using dilated convolution and skip connections. Experiments were performed using three publicly available datasets, i.e., CVC-ClinicDB, CVC-ColonDB, and ETIS-Larib Polyp DB. Experimental results showed that our generative network outperformed U-Net and can synthesize more realistic polyp images. The modified YOLOv4 significantly improved the performance of polyp detection using the expanded dataset, with an accuracy of 92.37% and a detection rate of 17.2 frames per second.